Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Hybrid Optimization for IRS-Aided Secure Communication: A Gradient Projection and Dynamic Security Framework","authors":"S. Sivasankar,&nbsp;S. Markkandan","doi":"10.1002/ett.70149","DOIUrl":"https://doi.org/10.1002/ett.70149","url":null,"abstract":"<div>\u0000 \u0000 <p>The emergence of advanced wireless communication technologies has heightened the need for effective physical-layer security, particularly in multi-user environments susceptible to eavesdropping. In this work, we propose a Hybrid Gradient Descent Optimization (HGDO) framework that leverages an Intelligent Reflecting Surface (IRS) to safeguard wireless transmissions. Our methodology features a hierarchical two-phase design, a Gradient Projection Method that jointly refines beamforming vectors and IRS phase shifts, and a Dynamic Security Region Expansion (DSRE) algorithm that reacts in real time to shifts in channel conditions. Simulation results confirm that the HGDO framework achieves notable improvements in secrecy rates, energy efficiency, and adaptability under diverse system settings. For instance, with 100 IRS elements, the solution attains a secrecy rate of 42 bps/Hz, showcasing its scalability and reliability. These findings suggest that HGDO holds strong promise for fortifying next-generation wireless networks against emerging security threats.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure Content Protection and Verification Framework for Digital Images Communication","authors":"Osama S. Faragallah,&nbsp;Walid El-Shafai,&nbsp;Osama Elshakankiry,&nbsp;Ashraf Afifi,&nbsp;Hala S. El-sayed","doi":"10.1002/ett.70131","DOIUrl":"https://doi.org/10.1002/ett.70131","url":null,"abstract":"<div>\u0000 \u0000 <p>Data security is an urgent and important dilemma in recent times because of the fast growth of the Internet and multimedia applications. Steganography, watermarking, and encryption techniques may be utilized to achieve data robustness and confidentiality. In this paper, the content-based image verification and data integrity technique are introduced. This technique is reliable and appropriate for confirming the reliability of images communicated through unreliable channels. First, the transferred image is divided into a number of dedicated blocks with different block sizes. Then, several discrete transformations like the DFT (Discrete Fourier Transforms), DWT (Discrete Wavelet Transform), and DCT (Discrete Cosine Transform) are employed and tested individually. They are utilized to insert a block-based watermark for every image block in an alternative image block corresponding to a certain mechanism. The marked image is transmitted over a wireless channel with various corruptions and attacks. On the receiver side, the embedded watermarks for each image block are obtained to distinguish dubious falsification behaviors. Various image analyses and comparisons are employed to test and examine the reliability of the proposed technique to accomplish superior content verification and safeguard images communicated over vulnerable transmission networks. Assessment findings approve the rightness of the suggested technique for different multimedia applications with confidential information categories like military services. In addition, comparative experimental findings and results demonstrate that the suggested content-based image verification and data integrity technique applying the DFT is the best protection framework contrasted with the DWT and DCT, as it achieves in the case of the Peppers image a low MSE of 6.1787, high PSNR of 46.2765 dB, and high C_r of 0.9933 at a 4 × 4 block size. These achievements ensure and confirm that the proposed method is an effective methodology for multimedia integrity and validation applications.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Secure Onlooker: Enabling End-to-End Security for Cloud Data Center by Hybrid VM Segmentation","authors":"Saravanan Kumarasamy,&nbsp;Santhosh Rajendran","doi":"10.1002/ett.70136","DOIUrl":"https://doi.org/10.1002/ett.70136","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud computing is an innovative technology that provides computing services over the internet and replaces the requirement to own physical hardware or software. Security threats present a wide range of risks to cloud computing, and a security threat defense plays a significant role in cloud computing. Virtual machines (VM) serve as the backbone, providing flexible and scalable resources for running and storing data. Moving Target Defense (MTD) and Blockchain enhance security and privacy by reducing the chances of successful attacks and minimizing the impact of security attacks. To address these issues, we propose integrating MTD and blockchain technologies within the cloud computing environment named Hybrid Secure Onlooker (HSO). The proposed work involves several entities, including Cloud Users (CUs), Centralized Subnet Manager (CSM), Distributed Group Manager (DGM), Consortium Block Module (CBM) and Private Block Module (PBM). Initially, we perform Multi-Factor Authentication (MFA) to establish secure communication and to avoid malicious traffic. Followed by this, we utilize the Komoda Miliphir optimization (KMO) algorithm to perform CUs' task scheduling based upon the task types, task sensitivity, and task size. Entrenched in the scheduled tasks, the CSM performs classification and grouping of cloud VMs, assigning them to their capacity, security protocols, and availability, utilizing the Residual Flowed Capsule Network (RFC-Net). The grouped subsets are overseen and managed by the DGM, which handles MTD operations such as virtual switch placement and VM migration within the subsets. Finally, the transactions are stored in the hybrid blockchain layer with CBM and PBM to ensure privacy and security. The is the implementation tool for realizing the proposed HSO model. The proposed model can be examined based on several metrics with state-of-the-art work comparisons. The results show that the proposed HSO model outperforms the state-of-the-art models.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT Data Transmission Security Using Blockchain With a Trust-Weighted Proof of Authority Consensus Mechanism in Healthcare","authors":"Shatakshi Kokate,&nbsp;Urmila Shrawankar","doi":"10.1002/ett.70139","DOIUrl":"https://doi.org/10.1002/ett.70139","url":null,"abstract":"<div>\u0000 \u0000 <p>Healthcare systems generate vast amounts of sensitive data, and ensuring its security and privacy remains a significant challenge. Various traditional solutions, including encryption techniques, access control mechanisms, and centralized cloud-based systems, have been developed to address these issues. However, these approaches often face challenges such as single points of failure, limited scalability, and lack of decentralized trust management. Encryption schemes such as AES and RSA are computationally expensive and do not inherently address issues of data integrity or transparency. At the same time, centralized systems are prone to security breaches and third-party vulnerabilities. This paper proposes a blockchain-based solution with a trust-weighted proof of authority (PoA) consensus mechanism to overcome these limitations and ensure secure data transmission in the Internet of Things (IoT)-enabled healthcare systems. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed for healthcare data classification, followed by Huffman encoding for data compression. Data are encrypted using a combination of the Substitution-Caesar cipher and Updated Elliptical Curve Cryptography (UECC). A novel trust-weighted PoA consensus mechanism is then used for block validation, enhancing the security of data transmission. The proposed solution achieves an accuracy of 99.38%, an F1 score of 98.2%, a precision of 98.5%, a recall of 98%, a throughput of 65 Mbps, a latency of 0.23 s, and a processing time of 17 s. The results demonstrate that the blockchain-based solution effectively addresses the shortcomings of traditional methods, ensuring higher security, scalability, and trust in healthcare data management.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel LC-LOA and S3R2GCNN-Based Dynamic Workflow Process Type Identification and Scheduling in Cloud","authors":"Sai Sathish Kethu,&nbsp;Dharma Teja Valivarthi,&nbsp;Sreekar Peddi,&nbsp;Swapna Narla,&nbsp;Durai Rajesh Natarajan,&nbsp;N. Purandhar","doi":"10.1002/ett.70129","DOIUrl":"https://doi.org/10.1002/ett.70129","url":null,"abstract":"<div>\u0000 \u0000 <p>Workflow scheduling (WS) maps out the processes and manages the execution of interdependent works within a process. However, the existing studies did not identify the workflow process types for efficient WS. Therefore, this paper presents a novel LC-LOA and S3R2GCNN-based dynamic workflow process type identification and scheduling in the cloud. Primarily, the cloud users register and log in with the cloud server. Afterward, the user assigns the workflow, followed by attribute extraction. Subsequently, the hashcode is generated for attributes by using THA. Next, the workflow deduplication is checked. If the workflow is repeated, it is removed from the workflow pool; otherwise, the workflow is given for WPTIS. In WPTIS, the classifier is trained based on data acquisition, graph slicing, attribute extraction, feature extraction, feature selection by LC-LOA, process labeling by L-Fuzzy, and classification by S3R2GCNN. Also, the features are extracted from the cloud server and given to WS. Eventually, the workflow is scheduled by using the Linear Congruential Lyrebird Optimization Algorithm (LC-LOA). The results show that the proposed system obtained a high accuracy of 98.43%, outperforming conventional models.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QDKFFHNet: Quantum Dilated Kronecker Feed Forward Harmonic Net for Intrusion Detection in IoT-Based Smart City Applications","authors":"Selvam Ravindran,&nbsp;Velliangiri Sarveshwaran","doi":"10.1002/ett.70141","DOIUrl":"https://doi.org/10.1002/ett.70141","url":null,"abstract":"<div>\u0000 \u0000 <p>In today's world, people are worried about keeping their information safe and secure. One essential way to keep safe is by employing a technique called intrusion detection (ID), which helps to find threats in networks while data is being sent. Deep learning (DL) aids the Internet of Things (IoT) and smart cities by creating devices that can think and make decisions on their own without human help. Hence, an effective approach named Quantum Dilated Kronecker Feed Forward Harmonic Net (QDKFFHNet) is introduced for ID in IoT-Based Smart City Applications. Initially, the system model is considered, and then, data collection is carried out. Thereafter, data normalization is conducted by linear normalization. After that, feature dimension transformation is conducted by information gain. Feature extraction is conducted. Moreover, feature conversion is conducted, and feature selection is performed by Mahalanobis distance and Wave-Hedges metric. Lastly, ID is done by employing QDKFFHNet, which is the combination of Quantum Dilated Convolutional Neural Network (QDCNN) and Deep Kronecker Network (DKN). It is noticed that QDKFFHNet has gained an accuracy of 92.69%, a negative predictive value (NPV) of 86.73%, a specificity of 91.77%, a sensitivity of 92.79%, and a positive predictive value (PPV) of 92.60%.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy Efficiency Optimization in Vehicle Communication Network Using Hybrid-Genetic Algorithm","authors":"Abdussalam Mubarak,&nbsp;Waqas Amin,&nbsp;Xuyang Shi","doi":"10.1002/ett.70146","DOIUrl":"https://doi.org/10.1002/ett.70146","url":null,"abstract":"<div>\u0000 \u0000 <p>In the current world of Intelligent Transportation System (ITS), Vehicle Communication Networks (VCNs) have a critical function of facilitating the communication of various ITS entities in vehicles, their environment, and other interconnected entities. Unfortunately, the dynamic and resource-constrained nature of VCNs creates major issues in the energy efficiency area, which is extremely important in extending the usable life of the network in those applications that incorporate battery-operated devices and/or mobile nodes. In this paper, a novel heuristic solution for the enhancement of the energy efficiency of VCNs is proposed and implemented using a Hybrid-Genetic Algorithm (HGA). The proposed algorithm combines the advantage of the normal Genetic Algorithms (GA) of a large search space while using local search to fine-tune results and result in fast convergence towards the optimum routing path. The goal is to reduce the overall energy expended in transmitting data, receiving data, and then processing this data while meeting reliability, delay, and throughput measures of Quality of Service (QoS). The proposed HGA achieved a 21% reduction in energy consumption compared to the Standard GA and a 13.6% reduction compared to Simulated Annealing, while maintaining high QoS standards. Simulation results are presented, which show the proposed algorithm incorporates balance between exploitative and explorative approaches such that routing paths in dynamic, challenging, and vehicular environments are achieved optimally. This research benefits the creation of sustainable VCNs that aid in the other objectives regarding efficiency in energy use and durability in ITS.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Iot-Fog-Cloud-Assisted Novel Industrial Disaster Evacuation Framework","authors":"Munish Bhatia","doi":"10.1002/ett.70147","DOIUrl":"https://doi.org/10.1002/ett.70147","url":null,"abstract":"<div>\u0000 \u0000 <p>Human evacuation is essential during calamities due to natural events, or deliberate acts. The convergence of numerous computing paradigms, Internet of Things (IoT), and Fog-Cloud computing holds significant promise for enhancing the evacuation process. This paper presents an advanced evacuation architecture that leverages the IoT-Fog-Cloud (IFC) computing model. The presented model uses IoT technology to perceive environmental data and track the mobility of occupants in real time. The fog computing module employs an Artificial Intelligence-driven Support Vector Machine (SVM) to determine emergent situations promptly. Moreover, the cloud layer provisions the development of an evacuation algorithm that generates safe and efficient routes based on real-time environmental conditions and occupant data collected from the fog module. This innovative approach provides a comprehensive evacuation map to guide individuals to designated safe locations. The effectiveness of this framework has been evaluated in a simulated environment comprising 8055 data segments. Results indicate significant improvements over existing methods, including: minimum delay (5.34 s), dimensional reduction (96.39%), classification performance (accuracy (94.89%), specificity (90.12%), sensitivity (95.02%), and F-measure (94.65%)), and energy efficiency (92%).</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Optimal Clustering-Based Congestion-Aware Multipath Routing Mechanism in WSN Using Hybrid Optimization and Adaptive Deep Network","authors":"S. Parthiban,&nbsp;C. Sivasankar,&nbsp;V. Sarala,&nbsp;U. Samson Ebenezar,&nbsp;Moorthy Agoramoorthy","doi":"10.1002/ett.70134","DOIUrl":"https://doi.org/10.1002/ett.70134","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless Sensor Networks (WSNs) are currently considered an effective distributed sensing technology that boosts the performance of integrated devices and wireless communication. Though WSN offers a novel opportunity for establishing the foundation for utilizing ubiquitous and pervasive computing, it faces some kinds of barriers and difficulties, such as low energy efficiency, data packet loss, and network latency. Especially due to repeatedly altered network design and congestion problems, it influences both network bandwidth utilization as well as efficiency. Therefore, in this work, an effectual congestion-aware multipath routing approach is implemented. The motivation behind this work is to resolve the critical issue of congestion-aware routing in WSNs, which is significant for effective data transmission as well as network performance. The enhancing demand for real-time data processing and transmission in WSNs has resulted in congestion-based issues such as energy depletion, delay, and packet loss. The conventional routing approaches mostly concentrate on optimizing single performance measures, avoiding the complex interplay among factors such as routing congestion, energy consumption, delay, and distance. To resolve these issues, the developed work suggests a Hybrid Heuristic-based Crayfish and Kookaburra Optimization Strategy (HH-CKOS), which comprises the Crayfish Optimization Algorithm (COA) and the Kookaburra Optimization Algorithm (KOA). The developed HH-CKOS algorithm chooses the Cluster Head (CH) from the node's group to enhance the performance of distance, delay, residual energy, energy consumption, load, path loss, and routing congestion. Furthermore, the Adaptive Deep Temporal Convolution Network (ADTCN) model is developed for monitoring the congestion and providing congestion-aware routing, where the parameters are tuned by the developed HH-CKOS algorithm to increase the performance. Finally, the developed system provides a congestion-detected outcome. At last, the performance of the developed system is explored and evaluated with numerous conventional systems and proves its superiority.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probability Model and Warning System for Improper Driving Behavior in Vehicle Ad Hoc Networks","authors":"Honglei Shen","doi":"10.1002/ett.70137","DOIUrl":"https://doi.org/10.1002/ett.70137","url":null,"abstract":"<div>\u0000 \u0000 <p>Intelligent transportation systems (ITS) have improved road safety and traffic management; however, improper driving behavior remains a main cause of accidents. Real-time detection and warning systems are crucial to proactively address and moderate these hazards. This research recommends a novel probability approach as well as a warning system for detecting improper driving behavior in Vehicle Ad Hoc Networks (VANETs). The system incorporates real-time data from onboard sensors, vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communication to observe driving behavior, like erratic lane changes, sudden acceleration, and harsh braking. It utilizes the Kalman Filtering (KF) technique and Interquartile Range (IQR) to remove noise and irrelevant data from the sensors and communication channels. This system establishes an Intelligent White Shark Optimized Support Vector Machine (IWSO-SVM) approach to detect improper driving behavior in VANETs. The IWSO-SVM method probabilistically assesses the probability of unsafe actions. When the probability of improper driver behavior exceeds a defined threshold, the system triggers an immediate warning to the driver and other nearby vehicles. The system in a real-world VANET integrates feedback loops from different sources to continuously improve the system's performance. The efficiency of the model is established through simulations, showcasing its ability to improve traffic flow, reduce accidents, and advance safer driving practices within the VANET environment. This classification offers a promising solution for real-time traffic safety monitoring, leveraging the ability of VANETs and advanced probability models to mitigate the risks of improper driving behavior.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}